Update app.py

app.py

@@ -1,3 +1,5 @@
+--- START OF FILE app.py ---
+
 # --- START OF FILE app.py ---
 
 import os
@@ -5,28 +7,26 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
-from dotenv import load_dotenv # Added
-from agent import build_graph # Added
-from langchain_core.messages import HumanMessage # Added
+from dotenv import load_dotenv
+from agent import build_graph
+from langchain_core.messages import HumanMessage
 
-load_dotenv() # Added
+load_dotenv()
 
-# (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Basic Agent Definition --- REMOVED THIS PART
 
-def run_and_submit_all( profile: gr.OAuthProfile | None):
+def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
     Fetches all questions, runs the LangGraph Agent on them, submits all answers,
     and displays the results.
     """
     # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
+    space_id = os.getenv("SPACE_ID")  # Get the SPACE_ID for sending link to the code
 
     if profile:
-        username= f"{profile.username}"
+        username = f"{profile.username}"
         print(f"User logged in: {username}")
     else:
         print("User not logged in.")
@@ -36,92 +36,102 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
-    # 1. Instantiate Agent ( modify this part to create your agent)
+    # 1. Instantiate Agent
     try:
         # Use the build_graph function from agent.py
-        agent_graph = build_graph() # Changed from BasicAgent()
+        agent_graph = build_graph()
         print("LangGraph agent initialized.")
     except Exception as e:
         print(f"Error instantiating agent graph: {e}")
         return f"Error initializing agent graph: {e}", None
     # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "Agent code link unavailable (SPACE_ID not set)" # Added a check for SPACE_ID
+    print(f"Agent code link: {agent_code}")
 
     # 2. Fetch Questions
-    # ... (rest of fetching code is the same) ...
     print(f"Fetching questions from: {questions_url}")
     try:
         response = requests.get(questions_url, timeout=15)
         response.raise_for_status()
         questions_data = response.json()
         if not questions_data:
-             print("Fetched questions list is empty.")
-             return "Fetched questions list is empty or invalid format.", None
+            print("Fetched questions list is empty.")
+            return "Fetched questions list is empty or invalid format.", None
         print(f"Fetched {len(questions_data)} questions.")
     except requests.exceptions.RequestException as e:
         print(f"Error fetching questions: {e}")
         return f"Error fetching questions: {e}", None
     except requests.exceptions.JSONDecodeError as e:
-         print(f"Error decoding JSON response from questions endpoint: {e}")
-         print(f"Response text: {response.text[:500]}")
-         return f"Error decoding server response for questions: {e}", None
+        print(f"Error decoding JSON response from questions endpoint: {e}")
+        print(f"Response text: {response.text[:500]}")
+        return f"Error decoding server response for questions: {e}", None
     except Exception as e:
         print(f"An unexpected error occurred fetching questions: {e}")
         return f"An unexpected error occurred fetching questions: {e}", None
 
-
     # 3. Run your Agent
     results_log = []
     answers_payload = []
     print(f"Running agent on {len(questions_data)} questions...")
-    print(f"--- Starting processing Task ID: {task_id}, Question: {question_text[:100]}...")
+
+    # Removed the problematic print statement from here
+
     for item in questions_data:
         task_id = item.get("task_id")
-        print(f"--- Finished agent invoke for Task ID: {task_id}")
-        
         question_text = item.get("question")
+
         if not task_id or question_text is None:
             print(f"Skipping item with missing task_id or question: {item}")
             continue
+
+        # Moved the print statement inside the loop, after task_id and question_text are assigned
+        print(f"--- Starting processing Task ID: {task_id}, Question: {question_text[:100]}...")
+
         try:
             # Invoke the LangGraph agent
             result_state = agent_graph.invoke({"messages": [HumanMessage(content=question_text)]})
 
             # Extract the final answer from the last message
-            submitted_answer = "Error: Agent did not provide a response." # Default in case extraction fails
-            print(f"--- Extracted answer for Task ID: {task_id}: {submitted_answer[:100]}...")
+            submitted_answer = "Error: Agent did not provide a response."  # Default in case extraction fails
+
             if result_state and "messages" in result_state and result_state["messages"]:
                 last_message = result_state["messages"][-1]
-                 # The final content is typically in the content attribute of the last message
+                # The final content is typically in the content attribute of the last message
                 if hasattr(last_message, 'content') and last_message.content:
                     submitted_answer = last_message.content
                 # else: Handle cases where the last message might be a tool message etc.,
                 # for simplicity, we just use the default error message if content is missing.
 
-             # Ensure submitted_answer is a string
+            # Ensure submitted_answer is a string
             if not isinstance(submitted_answer, str):
-                 submitted_answer = str(submitted_answer)
+                submitted_answer = str(submitted_answer)
 
             answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
+
+            # Moved this print statement inside the loop as well
+            print(f"--- Finished processing Task ID: {task_id}")
+            # Moved this print statement inside the loop as well
+            print(f"--- Extracted answer for Task ID: {task_id}: {submitted_answer[:100]}...")
+
+
         except Exception as e:
-             print(f"Error running agent graph on task {task_id}: {e}")
-             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+            print(f"Error running agent graph on task {task_id}: {e}")
+            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+            # Note: If an error occurs, the 'Finished' and 'Extracted answer' prints for this specific task won't happen,
+            # which is reasonable behavior.
 
     if not answers_payload:
         print("Agent did not produce any answers to submit.")
         # Even if no answers, show the log of errors
         return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
-
     # 4. Prepare Submission
     submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
     status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
     print(status_update)
 
     # 5. Submit
-    # ... (rest of submission code is the same) ...
     print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
     try:
         response = requests.post(submit_url, json=submission_data, timeout=60)
@@ -167,7 +177,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
 
 # --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# LangGraph Agent Evaluation Runner") # Updated title
+    gr.Markdown("# LangGraph Agent Evaluation Runner")  # Updated title
     gr.Markdown(
         """
         **Instructions:**
@@ -189,7 +199,6 @@ with gr.Blocks() as demo:
     run_button = gr.Button("Run Evaluation & Submit All Answers")
 
     status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    # Removed max_rows=10 from DataFrame constructor
     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
 
     run_button.click(
@@ -198,10 +207,10 @@ with gr.Blocks() as demo:
     )
 
 if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
+    print("\n" + "-" * 30 + " App Starting " + "-" * 30)
     # Check for SPACE_HOST and SPACE_ID at startup for information
     space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
+    space_id_startup = os.getenv("SPACE_ID")  # Get SPACE_ID at startup
 
     if space_host_startup:
         print(f"✅ SPACE_HOST found: {space_host_startup}")
@@ -209,15 +218,14 @@ if __name__ == "__main__":
     else:
         print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
 
-    if space_id_startup: # Print repo URLs if SPACE_ID is found
+    if space_id_startup:  # Print repo URLs if SPACE_ID is found
         print(f"✅ SPACE_ID found: {space_id_startup}")
         print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
         print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
     else:
         print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
 
-    print("-"*(60 + len(" App Starting ")) + "\n")
+    print("-" * (60 + len(" App Starting ")) + "\n")
 
-    print("Launching Gradio Interface for LangGraph Agent Evaluation...") # Updated message
-    # demo.launch(debug=True, share=False)
-    demo.launch(debug=True, share=False, auth=None) # Keep auth=None for public space or remove for gated
+    print("Launching Gradio Interface for LangGraph Agent Evaluation...")  # Updated message
+    demo.launch(debug=True, share=False, auth=None)